Find Academic Supervisors

• Business Process Management
• Process Mining
• Business Process Simulation
• +4 more

Marlon Dumas is a leading researcher in business process management and process mining at the University of Tartu, Estonia. With over 467 publications spanning from 1997 to 2025, his work has significantly advanced methodologies in business process analysis, simulation, and optimization. His research bridges theoretical foundations with practical applications, developing tools and frameworks that enable organizations to analyze and optimize operational processes. Dumas's primary research interests include business process management, process mining, business process simulation, prescriptive process monitoring, and data-aware business processes. He has pioneered methods for modeling resource availability, activity delays, and waiting times in business processes. His work on prescriptive process monitoring addresses critical challenges such as resource constraints, uncertainty in predictions, and causal effect estimation for interventions. Recent publications reveal a strong trend toward integrating artificial intelligence with business process management, particularly exploring the application of large language models to process optimization, monitoring, and redesign tasks. His research demonstrates consistent innovation, with publications appearing in top venues including Information Systems, Data & Knowledge Engineering, and the International Conference on Business Process Management. Dumas has developed several influential tools including SIMOD for automated discovery of business process simulation models, Optimos for simulation-driven process optimization, and Kairos for prescriptive monitoring. His collaborative network is extensive, featuring frequent co-authorship with prominent researchers including Marcello La Rosa, Luciano García-Bañuelos, Fabrizio Maria Maggi, and Wil M. P. van der Aalst. His work on privacy-preserving process mining, particularly regarding differentially private release of event logs, addresses critical challenges in applying process mining techniques while maintaining data privacy and compliance with regulations like GDPR. Dumas's research continues to push boundaries, with recent work exploring the integration of large language models with business process management systems, suggesting an ongoing commitment to advancing the field through innovative applications of emerging technologies.

• Positional games
• Discrete and computational geometry
• Discrete random structures
• +2 more

Miloš Stojaković is a Full Professor at the Department of Mathematics and Informatics, Faculty of Sciences, University of Novi Sad, Serbia. He has held this position since 2016, following roles as Associate Professor (2011–2016) and Assistant Professor (2006–2011). His research focuses on positional games, discrete and computational geometry, discrete random structures, combinatorial algorithms, and graph theory. He leads the Foundations of Computer Science group since 2018. Stojaković earned his Ph.D. in Computer Science from ETH Zurich (2005), advised by Emo Welzl and Tibor Szabó, and holds M.Sc. and B.Sc. degrees from the University of Novi Sad. He has been recognized with the Dr Z. Đinđić Award (2008) for best young scientist in Vojvodina and the Best Student of University of Novi Sad Award (1998/99). He has advised three Ph.D. students: Mirjana Mikalački, Marko Savić, and Jelena Stratijev. His work spans over 60 publications, including seminal contributions to positional games and computational geometry. He serves on editorial boards of Discrete Mathematics & Theoretical Computer Science and the Novi Sad Journal of Mathematics . Stojaković teaches courses such as Combinatorial Algorithms, Graph Theory, and Theoretical Computer Science at the University of Novi Sad. He has also taught specialized courses on positional games at institutions like the University of Buenos Aires and Eötvös Loránd University Budapest. His research interests emphasize algorithmic and combinatorial aspects of games, geometry, and graph theory.

Garofalakis MinosView profile

Professor

• Database Systems
• Data Streams
• Big Data Analytics
• +7 more

Minos Garofalakis is a Professor of Computer Science at the School of Electrical and Computer Engineering (ECE) of the Technical University of Crete (TUC), where he directs the Software Technology and Network Applications Laboratory (SoftNet). He is also the Director of the Information Management Systems Institute (IMSI) at the Athena Research and Innovation Centre in Athens. Previously, he held roles at Yahoo! Research, Intel Research Berkeley, and Bell Laboratories, and was an Adjunct Associate Professor at UC Berkeley. Education: He earned a BSc in Computer Engineering from the University of Patras (1992), followed by MSc (1994) and PhD (1998) in Computer Science from the University of Wisconsin-Madison. Research Interests: His work focuses on Big Data analytics , including database systems, data streams, approximate query processing, probabilistic databases, and secure/private data analytics. Key areas include distributed stream processing, data synopses, and machine learning applications. He has authored over 150 papers and holds 29 patents, with an h-index of 63 and 13,500+ citations. Recent Work Trends: His recent articles emphasize scalable stream analytics (e.g., OmniSketch), privacy-preserving techniques, and distributed event processing. He explores challenges in handling high-velocity data streams, uncertainty in databases, and real-world applications like healthcare analytics. Scientific Awards: ACM Fellow (2018), IEEE Fellow (2017), TUC Excellence Award (2015), and multiple patents from Bell Labs/Yahoo/AT&T. Advising & Grants: He led EU projects such as FERARI, LEADS, and The Human Brain Project. His lab, SoftNet, develops tools for extreme-scale analytics and declarative networking. Current work includes interactive cross-platform analytics (Infore) and AI-driven medical data systems. Labs/Teams: Director of SoftNet Lab and IMSI. Collaborates with industry partners on distributed systems and privacy-preserving technologies.

• Computational Neuroscience
• Machine Learning
• Cognitive Science
• +3 more

Qiong Zhang is an Assistant Professor jointly appointed in the Psychology Department and Computer Science Department at Rutgers University. Her research bridges human learning and machine learning, focusing on comparing human memory systems with AI solutions and informing intelligent machine design through cognitive insights. She holds a Ph.D. from Carnegie Mellon University's Machine Learning Department under the School of Computer Science and completed postdoctoral training at Princeton University. Education: Ph.D., Machine Learning, Carnegie Mellon University (School of Computer Science) Postdoctoral Training, Princeton University Her research investigates cognitive constraints in memory systems and their neural bases. She employs computational modeling, behavioral experiments, and neural imaging to explore how memory optimizes performance under constraints. Recent work includes an NSF-funded project on external cue benefits/detriments in memory search. Her lab's findings aim to enhance memory interventions and inform AI design. Grants and funding include support from the National Science Foundation and Rutgers Brain Health Institute. She explores interdisciplinary topics like episodic memory in storytelling (comparing humans and LLMs), and cognitive resource modeling during memory encoding.

Dmitry BerensonView profile

Associate Professor

• Robotics
• Motion Planning
• Machine Learning
• +3 more

Dmitry Berenson is an Associate Professor in the Robotics Department and Electrical Engineering and Computer Science Department at the University of Michigan. He holds a B.S. from Cornell University (2005) and a Ph.D. from Carnegie Mellon University (2011). His research focuses on algorithms for robotic manipulation, motion planning, and control, emphasizing integration with real-world systems and open-source distribution. He has received the IEEE RAS Early Career Award and NSF CAREER Award. His academic journey includes postdoctoral work at UC Berkeley (2012) and faculty positions at Worcester Polytechnic Institute (2012-2016). He leads the ARM Lab, exploring topics such as deformable object manipulation, tactile control, and learning-based planning. Teaching responsibilities include courses like ROB 502 (Programming for Robotics), EECS 465 (Algorithmic Robotics), and ROB 520 (Motion Planning). Education: B.S., Electrical and Computer Engineering, Cornell University (2005) Ph.D., Robotics Institute, Carnegie Mellon University (2011) Postdoctoral Research, UC Berkeley (2012) Research Interests: Learning and motion planning for manipulation Control theory and optimization Deformable object interaction Robot perception and tactile systems Key Contributions: Development of algorithms for manipulation under uncertainty Advances in motion planning with contact feedback Integration of learning with classical robotics methods Recent publications emphasize probabilistic modeling, tactile-driven control, and generalization in learned dynamics. His work addresses challenges in cluttered environments, deformable objects, and safe human-robot collaboration.

Marina RiabizView profile

Lecturer

• Computational Statistics
• Probabilistic Machine Learning
• Bayesian Inference
• +3 more

Dr. Marina Riabiz is a Lecturer in Statistics at King's College London's Department of Mathematics, within the Faculty of Natural, Mathematical & Engineering Sciences. She holds a PhD from the University of Cambridge and a Master’s in Mathematical Engineering from Politecnico di Milano. Her research focuses on computational statistics, probabilistic machine learning, Bayesian inference, and medical engineering applications. She co-leads the DT4Health Centre for Doctoral Training, which develops digital twins for healthcare innovation. Key projects include uncertainty quantification in cardiac models and virtual patient cohort simulations. Her work spans MCMC optimization, Gaussian approximation, and state space models with stable processes. She has contributed to high-impact journals and conferences, including Annual Review of Statistics and IEEE Transactions. Education: PhD in Signal Processing, University of Cambridge (UK) MSc in Mathematical Engineering, Politecnico di Milano (Italy) BSc in Mathematical Engineering, Politecnico di Milano (Italy) Research Interests: Bayesian computational methods Medical data science Uncertainty quantification Machine learning integration with statistical inference Publications Overview: Riabiz’s recent work emphasizes MCMC postprocessing, optimal thinning algorithms, and Gaussian approximation for stable noise systems. Her cardiac modelling research bridges computational statistics with biomedical applications, including electrophysiology model calibration and virtual patient cohorts. These contributions advance both theoretical statistics and translational healthcare technologies. Awards: No specific awards listed. Grants and Collaborations: Active in multidisciplinary teams at King’s College London and the Alan Turing Institute. Leads DT4Health’s recruitment and mentoring initiatives. Labs/Teams: Cardiac Electro-Mechanics Research Group (BMEIS), Centre for Doctoral Training in DT4Health.

Xuefei LUView profile

Associate Professor

• Statistical Machine Learning
• Uncertainty Quantification
• Big Data Problems
• +9 more

Xuefei LU is an Associate Professor at SKEMA Business School (France), affiliated with the SKEMA Center for Analytics and Management Science and the Digitalization Research Center. Her academic journey includes a Ph.D. in Statistics from Bocconi University (Italy) and an MSc in Analytics from the University of Manchester (UK). Prior roles include Assistant Professorships at SKEMA Business School and the University of Edinburgh Business School, along with a postdoctoral position at Politecnico di Milano (Italy). Her research focuses on Statistical Machine Learning , Uncertainty Quantification , and Big Data Problems , with applications in epidemiological modeling, decision analysis, and industrial systems. She has developed frameworks for identifying critical components in complex systems and pioneered methods like the Cohort Shapley value for fairness in SME financing. Her work has been recognized with prestigious awards, including the 2024 INFORMS Data Mining Best Paper Award (Runner-Up) and multiple Excellence in Reviewing Awards from the European Journal of Operational Research. She actively supervises doctoral students in areas like anomaly detection and pandemic modeling. Xuefei LU collaborates across disciplines, contributing to labs focused on Reliability Engineering , Molten Salt Reactors , and Transportation Systems . Her research bridges theoretical advancements with practical applications in public health, infrastructure resilience, and sustainable energy systems.

• Deep Learning
• Partial Differential Equations
• Physics-Informed Neural Networks
• +3 more

Katsiaryna Haitsiukevich is a Visitor (Faculty) and Doctoral Researcher in the Department of Computer Science at Aalto University. Her research focuses on deep learning applications in science and engineering, particularly at the intersection of partial differential equations and neural networks. She holds a doctoral degree (Tekn. toht., tietotekniikka) awarded in May 2025. Her work includes advancing physics-informed deep learning frameworks, improving neural network training through ensemble methods, and leveraging large language models for symbolic regression. Key contributions span dynamical systems analysis, parameter space exploration, and Hamiltonian trajectory learning. Since 2021, she has published six peer-reviewed articles in top conferences like ICANN, IJCNN, and ACL. Notable collaborations include projects with researchers like Prof. P. Marttinen and Prof. A. Ilin. Her research emphasizes practical applications in computational physics, mathematical modeling, and engineering systems.

Agoston PisztoraView profile

Associate Professor

• Probability Theory
• Statistical Mechanics
• Phase Transitions
• +3 more

Agoston Pisztora is an Associate Professor in the Department of Mathematical Sciences at Carnegie Mellon University, part of the Mellon College of Science. His research focuses on probabilistic analysis of random fields, disordered systems, and phase transitions, aiming to explain macroscopic phenomena through microscopic system analysis. He holds a Ph.D. from ETH Zürich and completed postdoctoral work at the Courant Institute, NYU. His research explores how random systems exhibit deterministic or stochastic behavior at macroscopic scales. Key areas include phase separation mechanisms in Ising models, percolation theory, and large deviation principles in disordered environments. His work bridges probability theory with statistical physics, addressing fundamental questions about self-averaging and critical phenomena. Notable publications include studies on Wulff crystals in Ising systems and surface-order large deviations. Though no awards or grants are explicitly listed, his contributions to phase transition theory and percolation are well-documented. No advising or lab affiliations are specified in the provided information.

• Robot Vision
• Robot Learning
• Computer Vision
• +5 more

Joni Kämäräinen is a Professor of Signal Processing at Tampere University’s Computing Sciences department under the Faculty of Information Technology and Communication Sciences. He leads the Vision Group and has a tenure-track career at Tampere University since 2012, achieving full professorship in 2020. Prior, he conducted postdoctoral research at the University of Surrey’s Center of Vision, Speech and Signal Processing and held a faculty position at LUT University. His research spans robot vision and robot learning , focusing on computer vision and machine learning applications. Key applied projects address industrial collaboration with Huawei, Nokia Technologies, and others, emphasizing robust visual perception, autonomous systems, and depth sensor integration. Notable work includes long-term visual place recognition, RGB-D tracking, and color constancy solutions. Recent publications (2023–2025) reflect expertise in digital twins for industrial manipulators, probabilistic subgoal modeling, depth-aware video deblurring, and LiDAR-place recognition datasets. His team’s outputs bridge robotics, AI, and signal processing, with interdisciplinary impacts in healthcare and autonomous navigation. Teaching responsibilities include graduate and doctoral colloquia, and the textbook Koneoppimisen perusteet (2023) . Funding sources include the Academy of Finland, EU Horizon 2020, Business Finland, Huawei, and Nokia Technologies.

• Sociolinguistics
• Sociophonetics
• Finnish Dialects
• +5 more

Liisa Mustanoja is a Senior University Lecturer at Tampere University's Faculty of Information Technology and Communication Sciences, within the Department of Language Studies. She leads the Languages Unit and directs the Doctoral Programme in Language Studies (DPLA). Her research focuses on sociolinguistics, sociophonetics, Finnish dialects, and historical language change, particularly analyzing wartime letters and affective speech. She heads the Plural research center and collaborates on projects like Tunteiden Tampere (Kone Foundation grant 2024–2027) and the Sulka corpus initiative. Her academic career includes a PhD in Language Studies and a Docent title. She has held leadership roles in ethics committees and networks like Langnet. She has received teaching awards (2021) and contributed over 38 publications since 2003, focusing on Finnish language variation, speech corpus development, and sociolinguistic methodology. Key grants: Kone Foundation grants for Sulka (2023–2026), Kippo (2017–2022), and Tunteiden Tampere (2024–2027) Research groups: Sumu (Sulka), Plural (Languages and Cultures) Her work bridges human-machine interaction in language, emphasizing ethical communication and inclusivity. Recent projects explore affective speech processing and language policy innovations like the 'Data Protection Passport' concept.

Xiaohui QiView profile

Assistant Professor

• Geotechnical Engineering
• Risk and Reliability Analysis
• Probabilistic Site Investigations
• +3 more

Dr. Xiaohui Qi is an Assistant Professor in the Department of Mechanical and Construction Engineering at Northumbria University since 2020. His research focuses on reliability analysis of geotechnical structures, probabilistic site investigations, and data-driven methods for predicting geotechnical and geological properties. Prior affiliations include postdoctoral roles at Nanyang Technological University (2017-2020) and the University of Macau (2015-2017), alongside a PhD from Wuhan University (2015) and an exchange at the National University of Singapore (2012-2015). His work addresses uncertainty in geotechnical engineering through Bayesian back-analysis, spatial variability characterization, and AI-driven predictive modeling. Key interests include slope stability, braced excavations, and soil parameter estimation using limited data. Recent research trends emphasize integrating machine learning (e.g., Bayesian methods, generalized additive models) with geostatistical techniques for 3D site reconstruction and geological interface prediction. His studies often compare methodologies (e.g., coupled Markov chains vs. stochastic simulations) to optimize spatial prediction accuracy. Collaborations span institutions in Singapore, China, and elsewhere, with a focus on applications like subway construction and foundation design uncertainties. No scientific awards are listed, but his 1625 citations highlight impactful contributions to geotechnical reliability and data analytics.

• Statistical Learning
• High-Dimensional Data Analysis
• Network Statistics
• +7 more

Charles Bouveyron is a Full Professor of Statistics at Université Côte d'Azur , Nice, France, and holds a Chair in Artificial Intelligence. He serves as Director of the Institut 3IA Côte d’Azur and leads the Inria research team MAASAI on Statistical Learning and Artificial Intelligence. He is an associate editor for The Annals of Applied Statistics and founded the Statlearn workshops . Research Interests: Statistical learning in high dimensions Learning on networks and functional data Deep latent variable models Adaptive learning with uncertain labels Applications in Medicine, Image Analysis, Astrophysics, and Humanities Notable Contributions: Developed multiple R packages including HDclassif , FisherEM , and FunLBM . Created the Linkage.fr platform for network analysis with textual edges. PhD Students: Current: Seydina Niang (Deep Generative Models), Kilian Burgi (Marine Diversity Monitoring), Baptiste Pouthier (Multimodal Learning) Former: Giulia Marchello (Dynamic Networks), Rémi Boutin (Network Analysis), Dingge Liang (Recommender Systems), Nicolas Jouvin (Latent Variable Models), Alexandre Saint-Dizier (Image Aggregation), Warith Harchaoui (Optimal Transport), Pierre-Alexandre Mattei (Sparse Clustering), Rawya Zreik (Temporal Networks), Anastasios Bellas (Anomaly Detection), Camille Brunet (Sparse Clustering) Contact: Email: charles.bouveyron@univ-cotedazur.fr / charles.bouveyron@inria.fr Postal: Equipe Maasai, Inria Sophia Antipolis, 2004 route des Lucioles, 06902 France

• Air Vehicle Design (Derivative and Unconventional)
• Computational Aerodynamics
• Aeroelasticity
• +2 more

Professor Andrea Da Ronch is a faculty member at the University of Southampton's School of Engineering, holding the academic rank of Professor and serving as Director of the Boeing Flight Simulators Laboratory. She maintains active research leadership and teaching responsibilities within the Aeronautics and Astronautics program, with a demonstrated commitment to high-quality education evidenced by two nominations for "Outstanding Lecturer" and Fellowship of the Higher Education Academy. Her research spans Air Vehicle Design (Derivative and Unconventional), Computational Aerodynamics, Aeroelasticity, Weather Modeling, and Multidisciplinary Optimisation. She develops scalable computational methods at the intersection of aerospace engineering, mathematics, and computer science to support next-generation sustainable air transportation, targeting aviation decarbonization, Advanced Air Mobility (AAM) connectivity, and accelerated industry product development through physics-informed machine learning approaches. Recent publications (2024-2025) reveal a pronounced trend toward geometric deep learning and graph neural networks for transonic flow prediction, morphing wing analysis, and propeller-wing interaction. These works emphasize computational efficiency in unsteady aerodynamics while addressing critical challenges in aircraft design validation and dynamic maneuver simulation. Scientific awards include: Rotary International Award (2012) Aerospace Speakers Travel Grant (2010) T.I.M.E. Double Degree Award (2008) AIAA Atmospheric Flight Mechanics Best Paper Award (2019) Fellow of the Higher Education Academy (2014) Senior Member of AIAA (2017) She currently supervises three PhD students (Declan Salazar Clifford, Gabriele Immordino, Giuseppe Morichetti) with funding secured from EPSRC, European Union H2020, US Air Force Office of Scientific Research, and Royal Academy of Engineering projects including IMPACT anti-ice coatings optimization, Sparsified Reduced Modeling, and transonic buffet analysis. As Director of the Boeing Flight Simulators Laboratory, she provides hands-on curriculum enhancement for engineering students. Her active membership in the Aerodynamics and Flight Mechanics Research Group facilitates collaboration on sustainable aviation initiatives while supporting her leadership in major projects like HOMER- with Professor Bharath Ganapathisubramani.

Yassine El MaazouzView profile

Lecturer

• Algebraic Geometry
• Non-Archimedean Geometry
• Commutative Algebra
• +2 more

Yassine El Maazouz is a Harry Bateman Postdoctoral Scholar in the Division of Physics, Mathematics and Astronomy at the California Institute of Technology (Caltech), with a faculty mentor appointment under Professor Matilde Marcolli. He holds instructor positions for graduate mathematics courses including MA132C (Topics in Algebraic Geometry) and MA140B (Probability). Previously, he completed his PhD in Statistics at UC Berkeley after studying pure and applied mathematics at École Polytechnique. Education: PhD in Statistics, UC Berkeley (2019-2023) Master's in Pure and Applied Mathematics, École Polytechnique (2016-2019) Research Interests: His work bridges algebraic/non-Archimedean geometry, commutative algebra, combinatorics, and mathematical physics. Primary investigations include: p-adic algebraic manifolds, isotropic vector gram matrices, tropical invariants, spinor-helicity varieties, and combinatorial probability models. Research consistently demonstrates innovative applications of algebraic methods to problems in theoretical physics and statistical mechanics. Publication Trends: Recent articles (2019-2025) show strong focus on: (1) Algebraic structures in high-energy physics (scattering amplitudes, Coulomb branches), (2) Computational methods for p-adic manifolds and tropical geometry, (3) Combinatorial probability models (Bernoulli polynomials, stochastic processes), and (4) Matrix algebras derived from valuation theory. Work frequently appears in top journals including SIAM Journal on Applied Algebra, Selecta Mathematica, and Combinatorics/PROBABILITY Computing. Awards: Harry Bateman Postdoctoral Scholarship (Simons Foundation) Teaching & Academic Service: At Caltech, teaches graduate courses in algebraic geometry and probability theory. Previously instructed statistics courses at UC Berkeley (2019-2022). Co-organizes the Information, Geometry & Physics Seminar featuring cutting-edge interdisciplinary research. Maintains extensive lecture notes on number theory, probability, and representation theory.